Towards Differentially Private Reinforcement Learning with General Function Approximation
📰 ArXiv cs.AI
Learn how to apply differential privacy to reinforcement learning with general function approximation, ensuring private and secure decision-making in complex environments.
Action Steps
- Apply the exponential mechanism to batched policy updates in RL algorithms to ensure differential privacy
- Use general function approximation to extend private RL beyond tabular and linear settings
- Analyze the regret of private RL algorithms using novel regret analysis techniques
- Implement batched policy update schemes to reduce the impact of noise on private RL algorithms
- Evaluate the trade-off between privacy and regret in private RL algorithms
Who Needs to Know This
Researchers and engineers working on reinforcement learning and privacy-preserving machine learning can benefit from this article, as it provides a foundation for developing private RL algorithms.
Key Insight
💡 Differential privacy can be applied to reinforcement learning with general function approximation, enabling private and secure decision-making in complex environments.
Share This
🤖️ Differentially private reinforcement learning with general function approximation is now possible! 📊️
Key Takeaways
Learn how to apply differential privacy to reinforcement learning with general function approximation, ensuring private and secure decision-making in complex environments.
Full Article
Title: Towards Differentially Private Reinforcement Learning with General Function Approximation
Abstract:
arXiv:2605.07049v1 Announce Type: cross Abstract: We present the first theoretical guarantees for differentially private online reinforcement learning (RL) with general function approximation, extending beyond prior work restricted to tabular and linear settings. Our approach combines a batched policy update scheme with the exponential mechanism, together with a novel regret analysis. We show that, even under general function approximation, the regret in the model-free setting under differential
Abstract:
arXiv:2605.07049v1 Announce Type: cross Abstract: We present the first theoretical guarantees for differentially private online reinforcement learning (RL) with general function approximation, extending beyond prior work restricted to tabular and linear settings. Our approach combines a batched policy update scheme with the exponential mechanism, together with a novel regret analysis. We show that, even under general function approximation, the regret in the model-free setting under differential
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